Automatic image captioning has achieved tremendous success over the past few years, where many methods[1]}, [2]}, [3]} are developed to accurately describe the visual objects and their relationships in the image. Recently, news image captioning has started to get increased attention with the focus on producing narration of news images with richer human-like information derived from the news article. The research on news image captioning is inspired by several recently collected benchmark datasets including BreakingNews [4]}, GoodNews [5]}, NY800KTimes [6]}, and VisualNews [7]}. Earlier works such as [4]} and [5]} propose a two-stage pipeline, where they first generate template captions and then insert named entities into corresponding positions. [10]} propose a hierarchical article encoding mechanism, where the model first retrieves the most relevant sentences and then attends to the appropriate words in the retrieved context with images involved in both steps. Later works, like [7]} and [6]} introduce end-to-end models that directly encode articles and images with pre-trained transformer architectures (BERT [13]} and RoBERTa [14]}), then generate the caption directly. The end-to-end method is better than the template-based approach as it models the rich contextual information in named entities using byte-pair encoding (BPE) [15]}. A recent work by [16]} proposes to follow journalistic guidelines when generating news image captions, i.e. each caption must contain the who, when, where, and how entities.
